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How Predictive Modeling Cuts Hospital Readmissions

Karen Minich-Pourshadi, for HealthLeaders Media, April 27, 2012

After gathering three years' worth of data, a team created a model that looked at inpatient units for general pediatrics based on pediatric early warning assessments and created scores using behavior, cardiovascular, and respiratory results. Scores of 3 or above linked to clear action and bedside exam by nurses or physicians, and scores of 7 or above linked to an automatic medical response team call.

The 523-licensed-bed CCHMC uses pediatric early warning scores in its predictive metric within its EPIC system to look at comorbidity, previous history, and risk, and then couples that information with the clinician's knowledge to assess the patient's risk level and put contingency plans in place should the worst-case scenarios develop.

"After doing this for a few years, we have achieved a systemwide approach to using at-risk predictions, and plans are in place to prevent potential risks from occurring," says Ryckman, who notes that the organization also uses the predictive model to determine the level of care coordination needed at discharge.

What the model showed was that while patients could be admitted with a wide variety of problems, there were common potential problems associated with each scenario; for instance, respiratory disease and pneumonia could be complicated by asthma. It would take the clinical staff's input to assess the likelihood of that taking place, and that information would be put into the data to help steer the computer toward deciding the at-risk level of the patient. To help assess the patients, the clinical team on each floor of the hospital meets three times a day (during shift changes) to assess the severity of patients' illnesses.

Also, the morning assessment looks at capacity and the potential for any patient to need intensive care. The staff also does a safety call that includes all the units and alerts the team to potential problems—for instance, if the pharmacy is low on a particular drug. This information is then paired with the technology, which supports the teams. For instance, during flu season the predictive model assesses the potential for added ER personnel and services, and creates targets for monitoring patient progress and when a patient escalation plan should take effect.

"We decided to use technology in a supportive role for the clinical staff, rather than as the solution. I believe other organizations could even run this exact approach in a hospital that has no EMR and the only thing they had was a legal pad and pencil," says Ryckman.

No additional staff was needed to run the predictive model program or coordinate the floor meetings that take place each day, he says. "Having these huddles isn't a hugely time-consuming process, and what comes out of it produces a good ROI," he adds."

The main goal of the program was to predict when children may be showing signs of progressive deterioration in their clinical condition and flag early on which patients may need escalated care. "By using this approach, we've seen the length of stay decrease in ICU, as has the number of critical care codes outside ICU," Ryckman says. The number of overall codes outside critical care averaged 20 events per 1,000 hospital days, with a single-quarter high reached in 2007 of over 40 events per 1,000 hospitals days. It now hovers near 10. "I'd say sending kids home sooner, with a shorter length of stay and not having complications, has an impact on our revenue stream, but the goal is to deliver better outcomes for better overall value of care. This eliminates preventable problems and takes waste out of our system," Ryckman explains.

Ryckman's peers at Parkland and Mount Sinai would agree. While for the most part predictive models require an organization putting some financial investment toward technology, it's not new technology—rather it's an investment in the EMRs they are required to have anyway. Organizations can take advantage of their data now and create predictive models that decrease their preventable admissions and improve outcomes and maximize capacity.

"The ROI with predictive modeling is difficult to characterize and analyze, but if you're preventing multiple admissions, then you're making beds available for other patients," Kalman says.


This article appears in the April 2012 issue of HealthLeaders magazine.

Reprint HLR0412-8

 


Karen Minich-Pourshadi is a Senior Editor with HealthLeaders Media.
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